Senior Staff Machine Learning Platform Engineer at Faire

Faire

Kitchener-Waterloo, ON; San Francisco, CA; Toronto, ON·3d ago

SeniorFull-timePythonAWSKubernetesDockerTerraformMySQLMachine LearningData SciencePyTorchCI/CD

Salary Range

$132,600 - $179,400 /year

Estimated

This salary is estimated based on similar roles. The actual salary may vary.

About the Role

We are looking for a Machine Learning Platform Engineer to join Faire's team. This role involves owning the technical vision and evolution of Faire’s ML platform, setting standards, and leading complex initiatives that unlock data science velocity at scale. You will architect systems that allow models to help small retailers compete and grow their businesses.

Key Responsibilities

→Define and drive the long-term architecture of Faire’s ML platform including training, inference, feature management, governance
→Establish company-wide standards for code quality, testing, MLOps, experimentation, model lifecycle management, and observability
→Lead adoption and advanced use of Unity Catalog, multi-workspace strategies, and data/ML mesh patterns
→Architect highly scalable ML workflows using Spark, Delta Lake, and MLflow
→Optimize performance, reliability, and cost of the ML platform
→Evaluate and integrate emerging Databricks features
→Stay ahead of the curve by engaging with the latest developments in machine learning and AI
→Serve as senior ML technical advisor to Faire’s data science and production engineering teams
→Represent Faire at ML conferences and meetups
→Mentor ML engineers and raise the overall bar for Machine Learning at Faire

Requirements

✓10-12 years of experience building and improving large-scale ML or data platforms
✓A degree (preferably graduate level) in Computer Science, Engineering, Statistics, or a related technical field
✓Deep expertise in Databricks lakehouse architecture, including governance via Unity catalog, orchestration via Workflows, and cost optimization
✓Proven ability to design systems that support multiple data science teams and production workloads
✓Strong background in distributed systems, ML infrastructure, and cloud architecture
✓Demonstrated technical leadership across teams and orgs; ability to influence without authority
✓Experience integrating LLM workflows into enterprise platforms is a plus
✓Previous contributions to open source ML Infrastructure projects or research publications is a very strong plus